Summary Over 1.7 million people suffer from limb loss in the United States and this number is estimated to increase by 185,000 per year for upper extremity loss. Robotic limbs are highly sophisticated, achieving similar movements compared to the human counterparts, can contain sensors that measure direction and speed of motion, and can be covered with advanced synthetic electronic skin that can detect changes in temperature, humidity, and pressure. Amputees can control their prosthetic limbs by implanted electrodes in their premotor cortex or in the peripheral nerves that can detect volitional intent and translate it into movement of the robotic limb. However, evoking a naturalistic sensation from the engineered sensors in the bionic limb remains a formidable challenge. A fundamental limitation lies in the fact that such signals are conveyed to the user through electrical stimulation of the peripheral nerves, which contain a mixed modality of motor and sensory populations, many of which are indiscriminately depolarized during electrical stimulation, thus producing abnormal tingling, buzzing or burning sensation. Specific sensory modality percepts such as proprioception, mechanoception, nociception or thermoception cannot be reproducibly elicited. Thus, despite much progress in electronic skin sensors and robotic prosthetic devices, this information cannot be naturistically conveyed to the users. This limitation is critical as it obligates users to rely on visual feedback to move and position their prosthetic limbs; such cognitive burden discourages the use of advanced robotic prosthesis. Therefore, there is a need to develop closed-looped interfaces that incorporate somatosensory information vital for achieving stable and adaptive motor control, particularly for grasp and manipulation tasks where visual feedback alone is insufficient. The goal of this study is to develop modality-defined neural interfaces capable of specifically recording from motor neurons and stimulating modality-specific sensory neurons with high selectivity and stability. We aim to 1) define the selectivity and potency of neuron- and glial-derived growth factors for in vivo chemotaxis of motor and sensory neurons; 2) to evaluate the degree by which axon type submodalities can be segregated from a regenerating mixed population nerve using competitive attractants; and 3) to decode movement intent, and evoke modality-specific sensation via molecularly guided neural interfacing.